Query building for search by ideal candidates

ABSTRACT

In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.

TECHNICAL FIELD

The present disclosure generally relates to computer technology forsolving technical challenges in search queries to data sources. Morespecifically, the present disclosure relates to the automatic buildingof queries for search using ideal candidates.

BACKGROUND

The rise of the Internet has occasioned two disparate phenomena: theincrease in the presence of social networks, with their correspondingmember profiles visible to large numbers of people, and the increase inuse of social networks for job searches, both by applicants and byemployers. Employers, or at least recruiters attempting to connectapplicants and employers, often perform searches on social networks toidentify candidates who have qualifications that make them goodcandidates for whatever job opening they are attempting to fill. Theemployers or recruiters then can contact these candidates to see if theyare interested in applying for the job opening.

Traditional querying of social networks for candidates involves theemployer or recruiter entering one or more search terms to manuallycreate the query. A key challenge in talent search is to translate thecriteria of a hiring position into a search query that leads to desiredcandidates. To fulfill this goal, the searcher has to understand whichskills are typically required for the position, what are thealternatives, which companies are likely to have such candidates, whichschools the candidates are most likely to graduate from, etc. Moreover,the knowledge varies over time. As a result, it is not surprising thateven for experienced recruiters, it often requires many searching trialsin order to obtain a satisfactory query.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the technology are illustrated, by way of exampleand not limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service, including a data processing module referred toherein as a search engine, for use in generating and providing searchresults for a search query, consistent with some embodiments of thepresent disclosure.

FIG. 3 is a block diagram illustrating an application server module ofFIG. 2 in more detail, in accordance with an example embodiment.

FIG. 4 is a block diagram illustrating a skills generator in moredetail, in accordance with an example embodiment.

FIG. 5 is a diagram illustrating an offline process to estimateexpertise scores, in accordance with another example embodiment.

FIG. 6 is a block diagram illustrating a search results ranker in moredetail, in accordance with an example embodiment.

FIG. 7 is a block diagram illustrating a search results ranker in moredetail, in accordance with another example embodiment.

FIG. 8 is a flow diagram illustrating a method for performing an idealcandidate-based search in accordance with an example embodiment.

FIG. 9 is a flow diagram illustrating generating a search query based onextracted one or more attributes, in accordance with an exampleembodiment.

FIG. 10 is a flow diagram illustrating a method of ranking searchresults using ideal candidates in accordance with an example embodiment.

FIG. 11 is a flow diagram illustrating a method for generating labelsfor sample ideal candidate member profiles in accordance with an exampleembodiment.

FIG. 12 is a flow diagram illustrating a method of dynamically trainingweights of a machine learning algorithm model in accordance with anexample embodiment.

FIG. 13 is a screen capture illustrating a first screen of a userinterface for performing an ideal candidate-based search in accordancewith an example embodiment.

FIG. 14 is a screen capture illustrating a second screen of the userinterface for performing an ideal candidate-based search in accordancewith an example embodiment.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Overview

The present disclosure describes, among other things, methods, systems,and computer program products that individually provide variousfunctionality. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various aspects of different embodimentsof the present disclosure. It will be evident, however, to one skilledin the art, that the present disclosure may be practiced without all ofthe specific details.

In an example embodiment, a system is provided whereby, given a set ofinput “ideal” candidates, a search query is built capturing the keyinformation in the candidates' profiles. The query is then used toretrieve and/or rank results. In this manner, a searcher may list one orseveral examples of good candidates for a given position. For instance,hiring managers or recruiters can utilize profiles of existing membersof the team for which the position pertains. In this new paradigm,instead of specifying a complex query capturing the positionrequirements, the searcher can simply pick up a small set of idealcandidates for the position. The system then builds a queryautomatically extracted from the input candidates and searches forresult candidates based on this built query. In some exampleembodiments, the automatically constructed query can also be presentedto the searcher, which helps explain why a certain result shows up in asearch ranking, making the system more transparent to the searcher.Further, the searcher can then interact with the system and have controlover the results by modifying the initial query.

It should be noted that the term “ideal” as used throughout the presentdisclosure is not intended to be any sort of measurement of desirabilityof a candidate. Rather, a candidate is simply labeled as “ideal” if asearcher has specified the candidate as a basis for the search. In otherwords, if the searcher feels that the candidate is ideal enough tospecify as a basis for the search, then that is enough for the candidateto be considered ideal for the systems and methods described herein.There is no necessity that that the candidate actually “be” ideal, norany measurement of how ideal a candidate is.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or awide area network (WAN)) to one or more clients. FIG. 1 illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An application program interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application server(s) 118host one or more applications 120. The application server(s) 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the application(s)120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the application(s)120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the client-server system 100 shown in FIG. 1 employs aclient-server architecture, the present disclosure is, of course, notlimited to such an architecture, and could equally well find applicationin a distributed, or peer-to-peer, architecture system, for example. Thevarious applications 120 could also be implemented as standalonesoftware programs, which do not necessarily have networkingcapabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplication(s) 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third party application 128, executing on athird party server 130, as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by a third party. The thirdparty website may, for example, provide one or more functions that aresupported by the relevant applications 120 of the networked system 102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices including, but notlimited to, a desktop personal computer (PC), a laptop, and a mobiledevice (e.g., a tablet computer, smartphone, etc.). In this respect, anyof these devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of the machines 110, 112 and the third party server130 may be a mobile device) to access and browse online content, such asany of the online content disclosed herein. A mobile server (e.g., APIserver 114) may communicate with the mobile app and the applicationserver(s) 118 in order to make the features of the present disclosureavailable on the mobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking service,including a data processing module referred to herein as a search engine216, for use in generating and providing search results for a searchquery, consistent with some embodiments of the present disclosure. Insome embodiments, the search engine 216 may reside on the applicationserver(s) 118 in FIG. 1. However, it is contemplated that otherconfigurations are also within the scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server 116) 212, which receives requests from variousclient computing devices, and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests or other web-based API requests. In addition, a memberinteraction detection module 213 may be provided to detect variousinteractions that members have with different applications 120,services, and content presented. As shown in FIG. 2, upon detecting aparticular interaction, the member interaction detection module 213 logsthe interaction, including the type of interaction and any metadatarelating to the interaction, in a member activity and behavior database222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in a data layer. In someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications 120and/or services provided by the social networking service.

As shown in FIG. 2, the data layer may include several databases, suchas a profile database 218 for storing profile data, including bothmember profile data and profile data for various organizations (e.g.,companies, schools, etc.). Consistent with some embodiments, when aperson initially registers to become a member of the social networkingservice, the person will be prompted to provide some personalinformation, such as his or her name, age (e.g., birthdate), gender,interests, contact information, home town, address, spouse's and/orfamily members' names, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the profile database 218. Similarly, when arepresentative of an organization initially registers the organizationwith the social networking service, the representative may be promptedto provide certain information about the organization. This informationmay be stored, for example, in the profile database 218, or anotherdatabase (not shown). In some embodiments, the profile data may beprocessed (e.g., in the background or offline) to generate variousderived profile data. For example, if a member has provided informationabout various job titles that the member has held with the sameorganization or different organizations, and for how long, thisinformation can be used to infer or derive a member profile attributeindicating the member's overall seniority level, or seniority levelwithin a particular organization. In some embodiments, importing orotherwise accessing data from one or more externally hosted data sourcesmay enrich profile data for both members and organizations. Forinstance, with organizations in particular, financial data may beimported from one or more external data sources and made part of anorganization's profile. This importation of organization data andenrichment of the data will be described in more detail later in thisdocument.

Once registered, a member may invite other members, or be invited byother members, to connect via the social networking service. A“connection” may constitute a bilateral agreement by the members, suchthat both members acknowledge the establishment of the connection.Similarly, in some embodiments, a member may elect to “follow” anothermember. In contrast to establishing a connection, the concept of“following” another member typically is a unilateral operation and, atleast in some embodiments, does not require acknowledgement or approvalby the member that is being followed. When one member follows another,the member who is following may receive status updates (e.g., in anactivity or content stream) or other messages published by the memberbeing followed, or relating to various activities undertaken by themember being followed. Similarly, when a member follows an organization,the member becomes eligible to receive messages or status updatespublished on behalf of the organization. For instance, messages orstatus updates published on behalf of an organization that a member isfollowing will appear in the member's personalized data feed, commonlyreferred to as an activity stream or content stream. In any case, thevarious associations and relationships that the members establish withother members, or with other entities and objects, are stored andmaintained within a social graph in a social graph database 220.

As members interact with the various applications 120, services, andcontent made available via the social networking service, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked, and informationconcerning the members' activities and behavior may be logged or stored,for example, as indicated in FIG. 2, by the member activity and behaviordatabase 222. This logged activity information may then be used by thesearch engine 216 to determine search results for a search query.

In some embodiments, the databases 218, 220, and 222 may be incorporatedinto the database(s) 126 in FIG. 1. However, other configurations arealso within the scope of the present disclosure.

Although not shown, in some embodiments, the social networking system210 provides an API module via which applications 120 and services canaccess various data and services provided or maintained by the socialnetworking service. For example, using an API, an application may beable to request and/or receive one or more navigation recommendations.Such applications 120 may be browser-based applications 120, or may beoperating system-specific. In particular, some applications 120 mayreside and execute (at least partially) on one or more mobile devices(e.g., phone or tablet computing devices) with a mobile operatingsystem. Furthermore, while in many cases the applications 120 orservices that leverage the API may be applications 120 and services thatare developed and maintained by the entity operating the socialnetworking service, nothing other than data privacy concerns preventsthe API from being provided to the public or to certain third partiesunder special arrangements, thereby making the navigationrecommendations available to third party applications 128 and services.

Although the search engine 216 is referred to herein as being used inthe context of a social networking service, it is contemplated that itmay also be employed in the context of any website or online services.Additionally, although features of the present disclosure are referredto herein as being used or presented in the context of a web page, it iscontemplated that any user interface view (e.g., a user interface on amobile device or on desktop software) is within the scope of the presentdisclosure.

In an example embodiment, when member profiles are indexed, forwardsearch indexes are created and stored. The search engine 216 facilitatesthe indexing and searching for content within the social networkingservice, such as the indexing and searching for data or informationcontained in the data layer, such as profile data (stored, e.g., in theprofile database 218), social graph data (stored, e.g., in the socialgraph database 220), and member activity and behavior data (stored,e.g., in the member activity and behavior database 222). The searchengine 216 may collect, parse, and/or store data in an index or othersimilar structure to facilitate the identification and retrieval ofinformation in response to received queries for information. This mayinclude, but is not limited to, forward search indexes, invertedindexes, N-gram indexes, and so on.

FIG. 3 is a block diagram illustrating the application server module 214of FIG. 2 in more detail. While in many embodiments the applicationserver module 214 will contain many subcomponents used to performvarious different actions within the social networking system 210, inFIG. 3 only those components that are relevant to the present disclosureare depicted. Here, a server profile search component 300 works inconjunction with a client profile search component 302 to perform one ormore searches on member profiles stored in, for example, profiledatabase 218. The server profile search component 300 may be, forexample, part of a larger software service that provides variousfunctionality to employers or recruiters. The client profile searchcomponent 302 may include a user interface and may be located on aclient device. For example, the client profile search component 302 maybe located on a searcher's mobile device or desktop/laptop computer. Insome example embodiments, the client profile search component 302 mayitself be, or may be a part of, a stand-alone software application onthe client device. In other example embodiments, the client profilesearch component 302 is a web page and/or web scripts that are executedinside a web browser on the client device. Regardless, the clientprofile search component 302 is designed to accept input from thesearcher and to provide visual output to the searcher.

In an example embodiment, the input from the client profile searchcomponent 302 includes an identification of one or more ideal candidatesfor a job opening. This identification may be accomplished in many ways.In some example embodiments, the input may be an explicit identificationof one or more member profiles stored in the profile database 218. Thisexplicit identification may be determined by the searcher, for example,browsing or otherwise locating specific profiles that the searcher feelsare ideal. For example, the searcher may know the identity ofindividuals on a team in which the open position is available, and maynavigate to and select the profiles associated with those teamindividuals. In another example embodiment, the searcher may create oneor more hypothetical “ideal candidate” profiles and use those as theinput. In another example embodiment, the searcher may browse or searchprofiles in the profile database 218 using traditional browsing orsearching techniques. In some example embodiments the explicitidentification may be provided by the job poster.

The server profile search component 300 may contain an attributeextractor 304. The attribute extractor 304 extracts raw attributes,including, for example, skills, companies, titles, schools, industries,etc., from the profiles of the one or more ideal candidates. These rawattributes are then passed to a query builder 306. For each attributetype, the query builder 306 aggregates the raw attributes across theinput candidates, expands them to similar attributes, and finallyselects the top attributes that best represent the ideal candidates.

After the query is generated, in an example embodiment the generatedquery may be shown to the searcher via the client profile searchcomponent 302 and the searcher may have the opportunity to edit thegenerated query. This may include adding to or removing some attributes,such as skills and companies, in the query. As part of this operation, aquery processor 308 may perform a search on the query and present rawresults to the searcher via the client profile search component 302.These raw results may be useful to the searcher in determining how toedit the generated query.

In an example embodiment, a machine learning model is trained to make“smart suggestions” to the searcher as to how to modify the generatedquery. The model may be trained to output suggestions based on anynumber of different facets, such as title, company, industry, location,school, and skill.

Usage data can be gathered regarding actions taken by searchers whenfacing a suggestion—(1) add the suggestion, (2) delete the suggestion,or (3) ignore the suggestion. Intuitively, if a searcher adds asuggestion it is probably a desired one and thus can be considered apositive training sample. If the searcher deletes the suggestion it isprobably not a desired one, and thus can be considered a negativetraining sample. For ignored suggestions, if the suggestion ispositioned lower than an added suggestion (e.g. “Santa Clara University”is positioned lower than added “University of California, Santa Cruz”),then it is not certain whether the suggestion is really ignored bysearchers or useless in the setting of the query. Thus, this data can beignored. If, however, the ignored suggestion is positioned higher thanan added suggestion, it can be treated as negative data.

After the query is modified, the query processor 308 may refresh thesearch results. A search results ranker 310 may act to rank the searchresults, taking into account both the query (including potentially thegenerated query and the modified generated query) as well as the inputideal candidates when ranking the search results.

Referring back to the query builder 306, given the raw attributes fromthe profiles of the ideal candidates, the query builder 306 generates aquery containing skills, companies, titles, etc. that best representsthe ideal candidates.

The query builder 306 may comprise a skills generator 312 designed togenerate skills to be added to the generated query. The socialnetworking service may allow members to add skills to their profiles.Typical examples of skills that, for example, an information technology(IT) recruiter might search could be “search,” “information retrieval,”“machine learning,” etc. Members may also endorse skills of othermembers in their network by, for example asserting that the member doesindeed have the specified skills. Thus, skills may be an important partof members' profiles that showcase their professional expertise. Atechnical challenge encountered, however, is that ideal candidates maynot explicitly list all of the skills they have on their profiles.Additionally, some of their skills may not be relevant to their coreexpertise. For example, an IT professional may list “nonprofitfundraising” as a skill.

To overcome these challenges, expertise scores for the ideal candidatemay be estimated based on explicit skills (skills the member hasexplicitly listed) as well as implicit skills (skills the member islikely to have, but has not explicitly linked).

FIG. 4 is a block diagram illustrating the skills generator 312 in moredetail, in accordance with an example embodiment. As shown in FIG. 4, ascoring apparatus 400 may calculate a set of expertise scores 402 usinga statistical model 404 and a set of features 406-408 for candidatemember profiles. Features 406-408 may be aggregated into a datarepository 410 from the member profiles and/or user actions. Forexample, features 406-408 may be received from a number of serversand/or data centers associated with the websites and/or applications andstored in a relational database for subsequent retrieval and use.

Prior to calculating expertise scores 402 on actual member profiles, atraining apparatus 412 may obtain training data for statistical model404, which includes a positive class 414 and a negative class 416.Positive class 414 may include data associated with items of aparticular category (e.g., trait, attribute, dimension, etc.), whilenegative class 416 may include data associated with items that do notbelong in the category.

For example, statistical model 404 may be a logistic regression modelthat classifies each member profile as either an expert or a non-expertin a corresponding skill. Positive class 414 may thus include a subsetof features 406-408 associated with members with known expertise in oneor more skills. Such “expert” members may be identified based onpublications, speeches, awards, and/or contributions of the users intheir respective fields. On the other hand, negative class 416 mayinclude a subset of features 406-408 associated with members who are notrecognized as experts in their respective fields, such as random memberswho list a given skill in their profiles. Because far fewer users belongin positive class 414 than negative class 416, positive class 414 may beoversampled to produce a roughly class-balanced set of training data forstatistical model 404.

Next, training apparatus 412 may use positive class 414 and negativeclass 416 to train statistical model 404. For example, trainingapparatus 412 may use maximum-likelihood estimation (MLE) and/or anotherestimation technique to estimate the parameters of a logistic regressionmodel for calculating expertise scores 402. After training of thelogistic regression model is complete, the parameters may be set so thatthe logistic regression model outputs values close to 1 for trainingdata in positive class 414 and values close to 0 for training data innegative class 416.

The trained statistical model 404 may be provided to scoring apparatus400, which calculates expertise scores 402 for member profiles notincluded in the training data (such as ideal member profiles supplied bythe searcher) by applying statistical model 404 to features (e.g.,features 406-408) for each of the items. For example, a feature vectormay be generated for each item from a subset of features 406-408 in datarepository 410, and statistical model 404 may be applied to the featurevector to calculate an expertise score for the item with respect to adimension of the member profile.

Features 406-408 used in the calculation of expertise scores 402 mayinclude demographic features, social features, and behavioral features.Demographic features may include data related to a user's location, age,experience, education, and/or background; social features may includefeatures related to the behavior of other users with respect to theuser; and behavioral features may include features related to the user'sactions or behavior with the online professional network and/or relatedwebsites or applications.

FIG. 5 is a diagram illustrating an offline process 500 to estimateexpertise scores, in accordance with another example embodiment. Asupervised machine learning algorithm combines various signals 502, suchas skill-endorsement graph page rank, skill-profile textual similarity,member seniority, etc. to estimate the expertise score. After this step,a formed expertise matrix 504 is very sparse since only a smallpercentage of the pairs can be predicted with any degree of certainty.Expertise matrix 504 may be factorized into member matrix 506 and skillmatrix 508 in K-dimensional latent space. Then, the dot-product of theexpertise matrix 504 and skill matrix 508 is computed to fill in the“unknown” cells. The intuition is that the more members who list twoparticular skills in their corresponding member profiles (calledco-occurrence of skills), the more likely it is that a member onlylisting one of those skills also has the other skill as a latent skill.Since the dot-product results in a large number of non-zero scores ofeach member on the skills, the scores can then be thresholded such thatif the member's score on a skill is less than a particular threshold,the member is assumed not to know the skill and is assigned a zeroexpertise score on the skill. Thus, the final expertise matrix 510 isstill sparse, but relatively much denser than formed expertise matrix504.

Referring back to FIG. 3, at run time, given a set of input idealcandidates IC, the skills generator 312 ranks the skills for the groupof ideal candidates using the formula:

${f({skill})} = {\sum\limits_{\Subset \in {IC}}{\exp\mspace{11mu}{{ertiseScore}\left( {c,{skill}} \right)}}}$

The top N skills are then selected to represent the ideal candidates.Expertise scores of an ideal candidate on outlier skills are zero orvery low, thus these skills are unlikely to be selected. Moreover, bytaking the sum over all candidates, the skills which many candidateshave are boosted, thus representing the commonality of the skill setamong all idea candidates.

Turning now to companies, given the ideal candidate profiles, besidetheir own companies, the query builder 306 can generate a set ofcompanies which are likely to have candidates similar to the idealcandidates in the ideal candidate profiles. In order to accomplish this,the query builder 306 contains a company generator 314, which usescollaborative filtering to find company relationships. Specifically, acompany browse map using co-viewing relationships (people who viewcompany A and also view company B) may be utilized. Intuitively,companies co-viewed by highly overlapped sets of people are likely to besimilar. Thus, activity and/or usage information for searchers/browserswithin the social networking service may be retrieved and mined toconstruct the company browse map, and this browse map may then be usedto find the company relationships by the company generator 314. Otherinformation may be used either in conjunction with or in lieu of thecompany browse map. For example, the social networking service may keeptrack of candidates that apply to a given company. Therefore, it maydeduce that if a user who applied to company B also applied to companyA, then company A and company B are similar. This similarityrelationship may be used like the browse map is used to generatecompanies related to companies identified in profiles of idealcandidates. Another signal that may be used is company movement, meaningthat if a large number of people who left company A went to work forcompany B, this might imply that company A and company B are somewhatsimilar.

Similar strategies can be used for other facets of a query. For example,title, industry, locations, and schools can all be expanded from thosefacets in the idea candidate profiles by finding similar facets using,for example, browse maps.

Once the query builder 306 completes building the query based on thetechniques described above, the query may be submitted to a searchengine to return search results. The hope, of course, is that the searchresults represent candidates who are similar in some ways to the idealcandidates submitted by the searcher, thus alleviating the searcher ofthe burden of composing the query. Once the results are returned, asearch results ranker 310 may rank the search results according to oneor more ranking algorithms. A subset of the top ranked search resultsmay then be displayed to the searcher using a results display component316. In an example embodiment, the results display component 316interacts with the client profile search component 302 to facilitatesuch a display. The number of top ranked search results displayed mayvary based on, for example, current size of a display window, font size,user preferences, etc.

While any ranking algorithms may be used by the search results ranker310 to rank the search results, in an example embodiment a machinelearning algorithm is used to train a ranking model specifically to beused with searches generated by searchers providing ideal candidates inlieu of text-based keywords. Given the significant difference between asearch by ideal candidates and a traditional query-based search, thisalgorithm helps provide rankings that accommodate this new type ofsearch.

FIG. 6 is a block diagram illustrating the search results ranker 310 inmore detail, in accordance with an example embodiment. The search querythat produced the search results, as well as the search results, may befed to a query-based feature producer 600, which produces a set ofquery-based features 602 of the results. Query-based features 602include search engine features such as term frequency-inverse documentfrequency (TF-IDF), term location in document, bag-of-words, etc. Thesequery-based features 602 may be fed to a query-based ranking model 604,which returns scores for each of the query/result pairs.

Separately, an ideal candidate (IC)-based feature producer 606 receivesas input the specified ideal candidate(s) and the search results fromthe query generated by the ideal candidate(s). The ideal candidate(IC)-based feature producer 606 then produces a set of idealcandidate-based features 608 of the results. Ideal candidate-basedfeatures 608 include features that are based on a comparison of idealcandidates and the search results (each feature measures one idealcandidate/search result pair). Example candidate-based features includesimilar career path, skill similarity, headline matching, headlinesimilarity, and browsemap similarity.

Similar career path is a measure of a trajectory similarity between thepositions held by the ideal candidate and the search result. Thus, forexample, if the ideal candidate started as an intern, was promoted to astaff engineer, and then was promoted to project manager, a searchresult having a similar progression of the trajectory of their careerpath would rank higher in this feature than, for example, a searchresult who started off at the top (e.g., as a project manager). Tocapture the trajectory information, each member profile may be modeledas a sequence of nodes, each of which records all information within aparticular position of a member's career, such as company, title,industry, time duration, and keyword summary.

At the node (position) level, similarity can then be ascertained byusing a generalized linear model, although in other embodiments otherapproaches could be substituted. Then, at the sequence (profile) level,a sequence alignment method may be employed to find an optimal ornear-optimal alignment between pairs of nodes from the two career paths.

Various schemes may be used to model the node corresponding to a jobposition, including sequence of positions and sequence of compositions.In the sequence of positions scheme, each node represents one particularposition of the member's professional experience. In the sequence ofcompositions scheme, for each node, in addition to using positioninformation, transition information is also incorporated between thegiven position and the previous one. In other words, the positioninformation, along with transition-related information, togethercomprise the node. Transition information, such as whether title changesin this transition, whether company changes, how the seniority changes,and the time in this transition, enhances the representation of thisscheme by further disclosing information of the changing trend between aprevious and a given position.

When evaluating the similarity between two career paths, each node is arepresentation of one particular work experience. In order to computethe overall similarity between two career sequences, the score may bedecomposed into the sum of the similarity between several pairs ofaligned nodes from the two sequences respectively. A sequence alignmentalgorithm may be used to measure the sequence level similarity bycalculating the sum of the optimal alignment of node pairs. The twosequences may be aligned incrementally. The sequence alignment schemecan be formulated as a dynamic programming procedure.

Suppose there are two career sequences P1=[X1;X2; ;Xm] and P2=[Y1;Y2;;Yn]. (Xi and Yj are position/composition nodes from two careersequences respectively.) Further, a step of aligning subsequences P1[1:i−1] and subsequence P2[1:j−1] may be encountered. (In other words,shorter subsequences have been aligned previously.) The subsequencesP1[1:i] and P2[1:j] can be aligned in three ways according to thefollowing cases:

(1) The node Xi is similar to node Yj. This leads to this pair ofpositions being aligned and results in an overall increase in sequencesimilarity score as contributed by this node similarity value. Here,P1[1:i] represents the subsequence X1, X2, . . . Xi from career sequenceP1.

(2) The node Xi is not very similar to node Yj. Thus, Xi will beskipped. Note that although a node is allowed to be skipped duringsequence alignment, contiguous alignment may be desirable for thepurpose of career path completeness. Therefore, a gap penalty may beimposed on sequence level similarity score when skipping a node.

(3) And vice versa: if the node Xi is not very similar to node Yj, thesame gap penalty may be imposed.

It should be noted that the position-level similarity function employedmay be symmetric. Hence, S^(node)(X_i, Y_j) is the same as S (Y_j, X_i).More formally, given the above two career sequences P1 and P2, thesimilarity between two career sequences can be solved using thefollowing scheme:

${S^{seq}\left( {{P_{1}\left\lbrack {1\text{:}i} \right\rbrack},{P_{2}\left\lbrack {1\text{:}j} \right\rbrack}} \right)} = {\max\left\{ \begin{matrix}{{S^{seq}\left( {{P_{1}\left\lbrack {{1\text{:}i} - 1} \right\rbrack},{P_{2}\left\lbrack {{i\text{:}j} - 1} \right\rbrack}} \right)} + {S^{node}\left( {X_{i},Y_{j}} \right)}} \\{{S^{seq}\left( {{P_{1}\left\lbrack {{1\text{:}i} - 1} \right\rbrack},{P_{2}\left\lbrack {1\text{:}j} \right\rbrack}} \right)} - \lambda} \\{{S^{seq}\left( {{P_{1}\left\lbrack {1\text{:}i} \right\rbrack},{P_{2}\left\lbrack {{1\text{:}j} - 1} \right\rbrack}} \right)} - \lambda}\end{matrix} \right.}$Therein, S^(seq) is the similarity function at the career sequencelevel, S^(node) is the similarity function at the position/compositionnode level, and λ is the gap penalty parameter.

A similarity model may be learned at the node level by using, forexample, a logistic regression model. Features relevant to this modelmay include, for example, current title, current company, currentcompany size, current industry, current functions, job seniority,current position summary, title similarity, company similarity, industrysimilarity, duration difference between positions, whether twotransitions were within the same company, whether two transitions werein the same industry, whether seniority changed, whether the titlechanged, and duration of time between the two transitions.

Skill similarity is a measure of similarity of the skill set of theideal candidate and the skill set of the search result. It should benoted that skill sets may include skills that are explicit (e.g.,specified by the member in their member profile) or implicit (e.g.,skills that are similar to skills specified by the member in theirmember profile, but not explicitly listed).

Headline matching is a measure of the similarity between the query andthe headline of each result. Notably, this is based on a text-basedcomparison, and is not strictly ideal candidate-based. A headline is oneor more visible fields (along with name) displayed as a search resultsnippet for a search result. While the concept of creating snippets foreach search result is a topic that is beyond the scope of the presentdisclosure, such snippets often include a headline that helps explainwhy the result is relevant and likely to trigger actions from thesearcher. The headline matching feature, therefore, measures thesimilarity between the query and this headline from the search result'ssnippet.

Headline similarity is a measure of the similarity between a headline ofthe ideal candidate and the headline of the search result. Thissimilarity calculation may be performed with or without considering wordsemantics. In example embodiments where word semantics are notconsidered, a word2vec algorithm may be utilized. Word3vec is a group ofrelated models used to produce word-embeddings. The word-embeddings areshallow, two-layer neural networks that are trained to reconstructlinguistic contexts of words. The neural network is shown a word andguesses which words occurred in adjacent position in an input text.After training, word2vect models can be used to map each word to avector of typically several hundred elements, which represent thatword's relation to other words.

Browsemap similarity is a measure of whether and how much othermembers/searchers/browsers visited both the ideal candidate's profileand the search result's profile in the same browsing session. Theintuition is that if previous members/searchers/browsers viewed bothprofiles in the same session, then there is a higher likelihood that theprofiles are similar, and thus that the underlying ideal candidate andsearch result are similar.

The ideal candidate-based features 608 may be fed along with the scoresfrom the query-based ranking model 604 to a machine learning algorithm610. The machine learning algorithm 610 is designed to train a combinedranking model 612 that is capable of determining a ranking score for asearch result at runtime. This training may use labels supplied fortraining data (e.g., training ideal candidates and training searchresults along with labeled scores for each). The training may involvethe machine learning algorithm 610 learning which features/scores aremore or less relevant to the ranking scores, and appropriately weightingsuch features and scores for runtime computations. At runtime, a featureextractor 614 extracts both query-based and ideal candidate-basedfeatures from the query, search results, and ideal candidates and feedsthese features to the combined ranking model 612, which produces thescores as per its model. A ranker 616 then uses these ranking scores torank the search results for display to the searcher.

It should be noted that since searching by ideal candidates is a newconcept, it is difficult to generate labeled data directly from a log ofprevious search systems, as would typically be done to generate labeleddata. Instead, in an example embodiment, labeled data is generated fromthe log of a query-based search. One such log is a log of electroniccommunications performed after the search. For example, if a searchersees 20 results to a query-based search for candidates, and sends emailcommunications to 8 candidates from the 20 results, then it may beassumed that these 8 candidates are similar enough to be considered forthe same job, and thus if a profile for one or more of those 8candidates had been submitted for a search by ideal candidate, the othercandidates could be considered likely top results. In an exampleembodiment, other actions taken with respect to previous search resultsmay be logged and similarly used to determine ideal candidate matches.For example, while communication with a candidate may be considered asstrongly indicative of a match for the underlying position (and thus amatch with other candidates also emailed for the same position) andassigned a high relevance score, clicking on a candidate (without anemail) may be considered to be a partial match and may be assigned amoderate relevance score, while skipped results might be considered alow relevance score. The relevance scores may be used as the labels forthe sample data.

Thus, in an example embodiment, communications between searchers andmembers of the social network service are monitored and logged and thesecommunications are used to derive a label score for each sample searchresult/ideal candidate pair (the sample search results may simply be thesearch results presented in response to previous queries). The labelscore may be generated using various combinations of the metricsdescribed above. For example, if the same searcher communicated withboth candidates A and B in response to the same search query, thencandidate B is assigned a score of 5 (on a scale of 1 to 5, 5 being mostrelevant) for an ideal candidate A and candidate A is assigned a scoreof 5 for an ideal candidate B. Actions such as clicking on a candidatethat indicate a moderate relevance may be assigned a score of 3 and noactions may be assigned a score of 1. Scores for various log entries canthen be combined and averaged. The result is profile pairs that havebeen assigned a score of between 1 and 5 based on previous actions orinactions by previous searchers. These label scores may then be used aslabels for hypothetical ideal candidate/search result pairs for thosesame member profiles.

In an example embodiment, a dynamic weight trainer is introduced intothe architecture of FIG. 6 in order to dynamically alter the weightsassigned to the IC-based features 608. Specifically, a search query neednot be limited to a single query and then the search is complete. Oftenthe searcher may interact with the original query and search result toprovide additional refinements of the original search. This is true notonly with traditional text-based searches but also can be true withideal candidate-based searches as well. This may be accomplished by thesearcher applying additional filters and or making text-based additionsto the initial ideal candidate-based search to refine the results. Theresult is that the role of the ideal candidate-based features, whichdirectly measure the similarity between the ideal candidate(s) and thesearch results, become less and less important as the search is refined.

At the same time, as the search session continues, the confidence of theremaining attributes (e.g., query-based attributes) increase inusefulness.

FIG. 7 is a block diagram illustrating the search results ranker 310 inmore detail, in accordance with another example embodiment. FIG. 7 isidentical to FIG. 6 with the exception of the addition of a dynamicweight trainer 700. The purpose of the dynamic weight trainer 700 is todynamically alter the weights of the features extracted to favor thequery-based features 602 over the ideal candidate-based features 608over time. This may be performed by applying a decay function, definedon some measure of session length, such as the number of queryrefinements, to gradually reduce the weights of the idealcandidate-based features 608 and/or increase the weights of thequery-based features 602. This function controls the dynamic balancebetween the impacts of the ideal input candidates and the query on theresult ranking.

FIG. 8 is a flow diagram illustrating a method 800 for performing anideal candidate-based search in accordance with an example embodiment.At operation 802, one or more ideal candidate documents may be obtained.In an example embodiment, these documents are member profiles in asocial networking service and they are obtained by a searcher specifyingthe corresponding members and the member profiles being retrieved from adatabase based on the searcher's specified members. However,implementations are possible where the documents obtained are not memberprofiles.

At operation 804, one or more attributes are extracted from the one ormore ideal candidate documents. At operation 806, a search query isgenerated based on the extracted one or more attributes. At operation808, a search is performed on documents using the generated searchquery, returning one or more result documents. Like with the idealcandidate documents, the result documents may also be member profiles ina social networking service.

FIG. 9 is a flow diagram illustrating generating a search query based onextracted one or more attributes, in accordance with an exampleembodiment. FIG. 9 corresponds to operation 806 of FIG. 8 in moredetail. At operation 900, the one or more attributes are aggregatedacross the one or more ideal candidate documents. At operation 902, theaggregated one or more attributes are expanded to include similarattributes. At operation 904, top attributes most similar to attributesof all of the one or more ideal candidate documents are selected. Atoperation 906, a set of expertise scores are calculated using astatistical model and a set of features regarding skills of the one ormore candidate documents. The statistical model may be a logisticregression model trained using a machine learning algorithm. Atoperation 908 the expertise scores are used to rank skills of the one ormore ideal candidate member profiles, using the top attributes. Atoperation 910, one or more top ranked skills are added to the searchquery.

At operation 912, a browse map is referenced. At operation 914, one ormore companies are added to the search query, the companies being oneswho have been co-viewed during the same browsing session as a companyidentified in one or more of the ideal candidate documents, by using thebrowse map.

FIG. 10 is a flow diagram illustrating a method 1000 of ranking searchresults using ideal candidates in accordance with an example embodiment.At operation 1002, one or more ideal candidate documents may beobtained. In an example embodiment, these documents are member profilesin a social networking service and they are obtained by a searcherspecifying the corresponding members and the member profiles beingretrieved from a database based on the searcher's specified members.However, implementations are possible where the documents obtained arenot member profiles.

At operation 1004, a search is performed using a search query, resultingone or more result documents. Like with the ideal candidate documents,the result documents may be member profiles in an example embodiment. Inone example embodiment, operation 1004 can be performed using some ofthe operations described above with respect to FIGS. 8 and 9.

At operation 1006, one or more query-based features are produced fromthe one or more result documents using the search query. As describedabove, this may include features such as TF-IDF.

At operation 1008, one or more ideal candidate-based features may beproduced from the one or more result documents using the one or moreideal candidate documents. As described above, the ideal candidate-basedfeatures may include similar career path, skill similarity, headlinematching, headline similarity, and/or browsemap similarity.

At operation 1010, the one or more query-based features and the one ormore ideal candidate-based features are input to a combined rankingmodel, outputting ranking scores for each of the one or more resultmember profiles. The combined ranking model may be trained using similarquery-based and ideal candidate-based features from sample resultdocuments as well as sample search queries and labels.

At operation 1012, the one or more result documents are ranked based onthe ranking score. At operation 1014, display of the one or more topranked result documents on a computer display is caused.

FIG. 11 is a flow diagram illustrating a method 1100 for generatinglabels for sample ideal candidate member profiles in accordance with anexample embodiment. At operation 1102, one or more sample idealcandidate member profiles in a social networking service are obtained.At operation 1104, one or more sample search result member profiles inthe social networking service are obtained. At operation 1106, for eachunique pair of sample ideal candidate member profile and sample searchresult member profile, a label is generated using a score generated fromlog information of the social networking service. The log informationincludes records of communications between a searcher and members of thesocial networking service, the score being higher if the searchercommunicated with both the member corresponding to the sample idealcandidate member profile and the member corresponding to the samplesearch result member profile in a same search session. The loginformation may further include records of user input by the searcher,the user input causing interaction with member profiles in the socialnetworking service but not resulting in communications between thesearcher and the member of the social networking service correspondingto both the sample ideal candidate member profile and the sample searchresult member profile in the same search session. An example wouldinclude clicking on member profiles and viewing the member profiles butnot emailing the corresponding members. A search session may be definedin a number of different ways. In one example embodiment, a searchsession is the same as a browsing session (e.g., as long as the searcheris logged in to the social networking service). In another exampleembodiment, the search session is limited to a period of time between asearcher initiating a search and the searcher submitting an unrelatedsearch or logging off the social networking service.

At operation 1108, the generated labels are fed into a machine learningalgorithm to train a combined ranking model used to output rankingscores for search result member profiles.

FIG. 12 is a flow diagram illustrating a method 1200 of dynamicallytraining weights of a machine learning algorithm model in accordancewith an example embodiment. At operation 1202, one or more idealcandidate documents are obtained. At operation 1204, a search isperformed using a search query, returning one or more result documents.This search query may or may not have been generated using the one ormore ideal candidate documents.

At operation 1206, one or more query-based features are produced fromthe one or more result documents using the search query. At operation1208, one or more ideal candidate-based features are produced from theone or more result documents using the one or more ideal candidatedocuments. At operation 1210, the one or more query-based features andthe one or more ideal candidate-based features are input to a combinedranking model. The combined ranking model is trained by a machinelearning algorithm to output a ranking score for each of the one or moreresult documents. The combined ranking model includes weights assignedto each of the one or more query-based features and each of the one ormore ideal candidate-based features.

At operation 1212, the one or more result documents are ranked based onthe ranking scores. At operation 1214, display of one or more top rankeddocuments on a computer display is caused. At operation 1216, one ormore refinements to the search are received. At operation 1218, theweights assigned to each of the one or more query-based features aredynamically trained to increase as more refinements are received, andthe weights assigned to each of the one or more ideal candidate-basedfeatures are dynamically trained to decrease as more refinements arereceived. This dynamic training may utilize a decay function based on,for example, time or number of refinements.

FIG. 13 is a screen capture illustrating a first screen 1300 of a userinterface for performing an ideal candidate-based search in accordancewith an example embodiment. The first screen 1300 includes an area 1302where a searcher can specify one or more ideal candidates for thesearch.

FIG. 14 is a screen capture illustrating a second screen 1400 of theuser interface for performing an ideal candidate-based search inaccordance with an example embodiment. The second screen 1400 presentsresults 1402 of the search, as well as displays the query generatedusing the specified ideal candidates, the query used for the search. Thequery may be displayed by highlighting terms of the query in variouscategories. For example, “software engineer” 1404 is a job title thatwas generated for the query, “python” 1406 is a skill that was generatedfor the query, and “Internet” 1408 is an industry that was generated forthe query. The searcher can then easily modify the query by addingadditional terms to the query and/or removing some of the identifiedterms that had been previously generated.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Machine and Software Architecture

The modules, methods, applications, and so forth described inconjunction with FIGS. 1-14 are implemented in some embodiments in thecontext of a machine and an associated software architecture. Thesections below describe representative software architecture(s) andmachine (e.g., hardware) architecture(s) that are suitable for use withthe disclosed embodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internet of things,” while yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere, as those of skill in the art can readily understand how toimplement the inventive subject matter in different contexts from thedisclosure contained herein.

Software Architecture

FIG. 15 is a block diagram 1500 illustrating a representative softwarearchitecture 1502, which may be used in conjunction with varioushardware architectures herein described. FIG. 15 is merely anon-limiting example of a software architecture, and it will beappreciated that many other architectures may be implemented tofacilitate the functionality described herein. The software architecture1502 may be executing on hardware such as a machine 1600 of FIG. 16 thatincludes, among other things, processors 1610, memory/storage 1630, andI/O components 1650. A representative hardware layer 1504 is illustratedand can represent, for example, the machine 1600 of FIG. 16. Therepresentative hardware layer 1504 comprises one or more processingunits 1506 having associated executable instructions 1508. Theexecutable instructions 1508 represent the executable instructions ofthe software architecture 1502, including implementation of the methods,modules, and so forth of FIGS. 1-14. The hardware layer 1504 alsoincludes memory and/or storage modules 1510, which also have theexecutable instructions 1508. The hardware layer 1504 may also compriseother hardware 1512, which represents any other hardware of the hardwarelayer 1504, such as the other hardware illustrated as part of themachine 1600.

In the example architecture of FIG. 15, the software architecture 1502may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1502may include layers such as an operating system 1514, libraries 1516,frameworks/middleware 1518, applications 1520, and a presentation layer1544. Operationally, the applications 1520 and/or other componentswithin the layers may invoke API calls 1524 through the software stackand receive responses, returned values, and so forth, illustrated asmessages 1526, in response to the API calls 1524. The layers illustratedare representative in nature and not all software architectures have alllayers. For example, some mobile or special purpose operating systemsmay not provide a layer of frameworks/middleware 1518, while others mayprovide such a layer. Other software architectures may includeadditional or different layers.

The operating system 1514 may manage hardware resources and providecommon services. The operating system 1514 may include, for example, akernel 1528, services 1530, and drivers 1532. The kernel 1528 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1528 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1530 may provideother common services for the other software layers. The drivers 1532may be responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1532 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 1516 may provide a common infrastructure that may beutilized by the applications 1520 and/or other components and/or layers.The libraries 1516 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 1514functionality (e.g., kernel 1528, services 1530, and/or drivers 1532).The libraries 1516 may include system libraries 1534 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 1516 may include API libraries 1536 such asmedia libraries (e.g., libraries to support presentation andmanipulation of various media formats such as MPEG4, H.264, MP3, AAC,AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that maybe used to render 2D and 3D graphic content on a display), databaselibraries (e.g., SQLite that may provide various relational databasefunctions), web libraries (e.g., WebKit that may provide web browsingfunctionality), and the like. The libraries 1516 may also include a widevariety of other libraries 1538 to provide many other APIs to theapplications 1520 and other software components/modules.

The frameworks 1518 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 1520 and/or other software components/modules. For example,the frameworks 1518 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 1518 may provide a broad spectrum of otherAPIs that may be utilized by the applications 1520 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 1520 include built-in applications 1540 and/or thirdparty applications 1542. Examples of representative built-inapplications 1540 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third party applications 1542 may includeany of the built-in applications 1540 as well as a broad assortment ofother applications. In a specific example, the third party application1542 (e.g., an application developed using the Android™ or iOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as iOS™, Android™, Windows® Phone, or other mobileoperating systems. In this example, the third party application 1542 mayinvoke the API calls 1524 provided by the mobile operating system suchas the operating system 1514 to facilitate functionality describedherein.

The applications 1520 may utilize built-in operating system 1514functions (e.g., kernel 1528, services 1530, and/or drivers 1532),libraries 1516 (e.g., system libraries 1534, API libraries 1536, andother libraries 1538), and frameworks/middleware 1518 to create userinterfaces to interact with users of the system. Alternatively, oradditionally, in some systems, interactions with a user may occurthrough a presentation layer, such as the presentation layer 1544. Inthese systems, the application/module “logic” can be separated from theaspects of the application/module that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 15, this is illustrated by a virtual machine 1548. A virtualmachine creates a software environment where applications/modules canexecute as if they were executing on a hardware machine (such as themachine 1600 of FIG. 16, for example). A virtual machine is hosted by ahost operating system (e.g., operating system 1514 in FIG. 15) andtypically, although not always, has a virtual machine monitor 1546,which manages the operation of the virtual machine 1548 as well as theinterface with the host operating system (e.g., operating system 1514).A software architecture executes within the virtual machine 1548, suchas an operating system 1550, libraries 1552, frameworks/middleware 1554,applications 1556, and/or a presentation layer 1558. These layers ofsoftware architecture executing within the virtual machine 1548 can bethe same as corresponding layers previously described or may bedifferent.

Example Machine Architecture and Machine-Readable Medium

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. Theinstructions 1616 transform the general, non-programmed machine into aparticular machine programmed to carry out the described and illustratedfunctions in the manner described. In alternative embodiments, themachine 1600 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine1600 may operate in the capacity of a server machine or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1600 maycomprise, but not be limited to, a server computer, a client computer, aPC, a tablet computer, a laptop computer, a netbook, a set-top box(STB), a personal digital assistant (PDA), an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), other smart devices, a web appliance, a network router, anetwork switch, a network bridge, or any machine capable of executingthe instructions 1616, sequentially or otherwise, that specify actionsto be taken by the machine 1600. Further, while only a single machine1600 is illustrated, the term “machine” shall also be taken to include acollection of machines 1600 that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610, memory/storage 1630, andI/O components 1650, which may be configured to communicate with eachother such as via a bus 1602. In an example embodiment, the processors1610 (e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), anotherprocessor, or any suitable combination thereof) may include, forexample, a processor 1612 and a processor 1614 that may execute theinstructions 1616. The term “processor” is intended to includemulti-core processors that may comprise two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously. Although FIG. 16 shows multipleprocessors 1610, the machine 1600 may include a single processor with asingle core, a single processor with multiple cores (e.g., a multi-coreprocessor), multiple processors with a single core, multiple processorswith multiples cores, or any combination thereof.

The memory/storage 1630 may include a memory 1632, such as a mainmemory, or other memory storage, and a storage unit 1636, bothaccessible to the processors 1610 such as via the bus 1602. The storageunit 1636 and memory 1632 store the instructions 1616 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1616 may also reside, completely or partially, within thememory 1632, within the storage unit 1636, within at least one of theprocessors 1610 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1600. Accordingly, the memory 1632, the storage unit 1636, and thememory of the processors 1610 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions 1616. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 1616) for execution by a machine (e.g.,machine 1600), such that the instructions, when executed by one or moreprocessors of the machine (e.g., processors 1610), cause the machine toperform any one or more of the methodologies described herein.Accordingly, a “machine-readable medium” refers to a single storageapparatus or device, as well as “cloud-based” storage systems or storagenetworks that include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1650 may include many other components that are not shown in FIG. 16.The I/O components 1650 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1650 mayinclude output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentalcomponents 1660, or position components 1662, among a wide array ofother components. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detect concentrations of hazardous gases for safetyor to measure pollutants in the atmosphere), or other components thatmay provide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672, respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components.Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1680may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN,a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet,a portion of the Internet, a portion of the Public Switched TelephoneNetwork (PSTN), a plain old telephone service (POTS) network, a cellulartelephone network, a wireless network, a Wi-Fi® network, another type ofnetwork, or a combination of two or more such networks. For example, thenetwork 1680 or a portion of the network 1680 may include a wireless orcellular network and the coupling 1682 may be a Code Division MultipleAccess (CDMA) connection, a Global System for Mobile communications(GSM) connection, or another type of cellular or wireless coupling. Inthis example, the coupling 1682 may implement any of a variety of typesof data transfer technology, such as Single Carrier Radio TransmissionTechnology (1×RTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (GPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long range protocols, or other data transfertechnology.

The instructions 1616 may be transmitted or received over the network1680 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1664) and utilizing any one of a number of well-known transfer protocols(e.g., HTTP). Similarly, the instructions 1616 may be transmitted orreceived using a transmission medium via the coupling 1672 (e.g., apeer-to-peer coupling) to the devices 1670. The term “transmissionmedium” shall be taken to include any intangible medium that is capableof storing, encoding, or carrying the instructions 1616 for execution bythe machine 1600, and includes digital or analog communications signalsor other intangible media to facilitate communication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:causing display, in a graphical user interface, of identifications of aplurality of member profiles, each of the member profiles comprising apage, renderable by a user interface, containing attributes of adifferent member of an online service: receiving, via the graphical userinterface, a selection of a plurality of the identifications, each ofthe plurality of identifications corresponding to a different memberprofile from the plurality of member profiles and identifying thecorresponding member profile as one of a plurality of ideal candidatemember profiles; extracting, by a hardware processor, one or moreattributes from the plurality of ideal candidate member profiles;generating, by the hardware processor, a search query based on theextracted one or more attributes, the generating comprising: forming anexpertise matrix, the expertise matrix containing an expertise score foreach of a plurality of combinations of member profiles and skills formembers of the online service, the expertise score for a givencombination of member profile and skill indicating a level of expertisefor a corresponding member in a corresponding skill; factorizing theexpertise matrix into a member matrix and a skills matrix; ranking theone or more attributes from the plurality of ideal candidate memberprofiles based on the expertise matrix and the skills matrix; andselecting top N attributes based on the ranking; modifying the generatedsearch query to include the selected top N attributes; and performing asearch on member profiles in the online service using the modifiedgenerated search query, returning one or more result member profiles. 2.The method of claim 1, wherein the expertise scores are generated usinga supervised machine learning algorithm that combines a plurality ofsignals, the signals comprising a skill-endorsement graph page rank, askill-profile textual similarity and member seniority.
 3. The method ofclaim 2, wherein the machine learning algorithm uses logisticregression.
 4. The method of claim 1, wherein the factorizing includesmapping the member matrix and the skill matrix in a K-dimensional latentspace.
 5. The method of claim 1, further comprising assigning anexpertise score of zero to a particular combination of member profileand skill if a member corresponding to the member profile has acalculated expertise score in the skill below a threshold.
 6. The methodof claim 5, further comprising training a machine learning model to makesuggestions of query modifications based on one or more facets, thetraining using usage data indicative of whether users added asuggestion, deleted a suggestion, or ignored a suggestion.
 7. The methodof claim 2, wherein the supervised machine learning algorithm classifieseach member profile as either an expert or a non-expert in acorresponding skill, based at least partially on publications inrespective fields.
 8. The method of claim 6, wherein the trainingincludes using a log from a query-based search.
 9. The method of claim1, further comprising: calculating a dot product of the expertise matrixand the skill matrix.
 10. The method of claim 1, further comprising:ranking the one or more result member profiles by: computing a careerpath similarity between each of the plurality of ideal candidate memberprofiles and each of the one or more result member profiles; feeding thecomputed career path similarity and each of the one or more resultmember profile into a machine learned model trained by a machinelearning algorithm to output a similarity score based on career pathsimilarity, the similarity score based on trajectory informationincluding a model of a member profile as a sequence of nodes, whereineach node records company, title, industry, and time durationinformation for a particular position of a corresponding member'scareer; and ordering the one or more result member profile by theircorresponding similarity score.
 11. The method of claim 10, wherein themachine learned model is a generalized linear model.
 12. The method ofclaim 10, wherein the machine learned model models each member profileas a sequence of nodes, wherein each node records information about adifferent position in a members career.
 13. A system comprising: acomputer-readable medium having instructions stored thereon, which, whenexecuted by a processor, cause the system to: receive, via the graphicaluser interface, a selection of a plurality of the identifications, eachof the plurality of identifications corresponding to a different memberprofile from the plurality of member profiles and identifying thecorresponding member profile as one of a plurality of ideal candidatemember profiles; extract, by a hardware processor, one or moreattributes from the plurality of ideal candidate member profiles;generate, by the hardware processor, a search query based on theextracted one or more attributes, the generating comprising: form anexpertise matrix, the expertise matrix containing an expertise score foreach of a plurality of combinations of member profiles and skills formembers of the online service, the expertise score for a givencombination of member profile and skill indicating a level of expertisefor a corresponding member in a corresponding skill; factorize theexpertise matrix into a member matrix and a skills matrix; rank the oneor more attributes from the plurality of ideal candidate member profilesbased on the expertise matrix and the skills matrix; and select top Nattributes based on the ranking; modify the generated search query toinclude the selected top N attributes; and perform a search on memberprofiles in the online service using the modified generated searchquery, returning one or more result member profiles.
 14. The system ofclaim 13, wherein the generating a search query comprises: aggregatingthe one or more attributes across the one or more ideal candidate memberprofiles; and selecting top attributes most similar to attributes of allof the one or more ideal candidate member profiles.
 15. The system ofclaim 13, wherein the generating includes: calculating a set ofexpertise scores using a statistical model and a set of featuresregarding skills of the one or more ideal candidate member profiles, thestatistical model trained using a machine learning algorithm; using theexpertise scores to rank skills of the one or more ideal candidatemember profiles; and adding one or more top ranked skills to the searchquery.
 16. The system of claim 15, wherein the statistical model is alogistic regression model.
 17. The system of claim 13, wherein thegenerating includes: referencing a second browse map, the second browsemap identifying companies having employees whose corresponding memberprofiles have been viewed during a same browsing session, to identifyco-viewed companies; and adding, to the search query, one or morecompanies that have been co-viewed during the same browsing session as acompany identified in one or more of the ideal candidate memberprofiles, using the second browse map.
 18. The system of claim 13,wherein the instructions further cause the system to submit the searchquery to a searcher for editing.
 19. A non-transitory machine-readablestorage medium comprising instructions, which when implemented by one ormore machines, cause the one or more machines to perform operationscomprising: causing display, in a graphical user interface, ofidentifications of a plurality of member profiles, each of the memberprofiles comprising a page, renderable by a user interface, containingattributes of a different member of an online service; receiving, viathe graphical user interface, a selection of a plurality of theidentifications, each of the plurality of identifications correspondingto a different member profile from the plurality of member profiles andidentifying the corresponding member profile as one of a plurality ofideal candidate member profiles; extracting, by a hardware processor,one or more attributes from the plurality of ideal candidate memberprofiles; generating, by the hardware processor, a search query based onthe extracted one or more attributes, the generating comprising formingan expertise matrix, the expertise matrix containing an expertise scorefor each of a plurality of combinations of member profiles and skillsfor members of the online service, the expertise score for a givencombination of member profile and skill indicating a level of expertisefor a corresponding member in a corresponding skill; factorizing theexpertise matrix into a member matrix and a skills matrix; ranking theone or more attributes from the plurality of ideal candidate memberprofiles based on the expertise matrix and the skills matrix; andselecting top N attributes based on the ranking; modifying the generatedsearch query to include the selected top N attributes; and performing asearch on member profiles in the online service using the modifiedgenerated search query, returning one or more result member profiles.20. The non-transitory machine-readable storage medium of claim 19,wherein the generating the search query comprises: aggregating the oneor more attributes across the one or more ideal candidate memberprofiles; and selecting top attributes most similar to attributes of allof the one or more ideal candidate member profiles.
 21. Thenon-transitory machine-readable storage medium of claim 19, wherein thegenerating includes: calculating a set of expertise scores using astatistical model and a set of features regarding skills of the one ormore ideal candidate member profiles, the statistical model trainedusing a machine learning algorithm; using the expertise scores to rankskills of the one or more ideal candidate member profiles; and addingone or more top ranked skills to the search query.
 22. Thenon-transitory machine-readable storage medium of claim 21, wherein thestatistical model is a logistic regression model.
 23. The non-transitorymachine-readable storage medium of claim 19, wherein the generatingincludes: referencing a second browse map, the second browse mapidentifying companies having employees whose corresponding memberprofiles have been viewed during a same browsing session, to identifyco-viewed companies; and adding, to the search query, one or morecompanies that have been co-viewed during the same browsing session as acompany identified in one or more of the ideal candidate memberprofiles, using the second browse map.